Complex-Valued Neurocomputing and Singular Points

Abstract

Context: Recently, the singular points of neural networks have attracted attention from the artificial intelligence community, and their interesting properties have been demonstrated. The objective of this study is to provide an overview of studies on the singularities of complex-valued neural networks. Evidence Acquisition: This review is based on the relevant literature on complex-valued neural networks and singular points. Results: Review of the studies and available literature on the subject area shows that the singular points of complex-valued neural networks have negative effects on learning, as do those of real-valued neural networks. However, the nature of the singular points in complex-valued neural networks is superior in quality, and the methods for improving the learning performance have been proposed. Conclusions: A complex-valued neural network could be a promising learning method from the viewpoint of a singularity.